The field of the invention is medical imaging and particularly, methods for producing images using a highly constrained image reconstruction method.
Recently a new image reconstruction method known in the art as “HYPR” and described in co-pending U.S. patent application Ser. No. 11/482,372, filed on Jul. 7, 2006 and entitled “Highly Constrained Image Reconstruction Method” was disclosed and is incorporated by reference into this application. With the HYPR method a composite image is reconstructed from acquired data to provide a priori knowledge of the subject being imaged. This composite image is then used to highly constrain the image reconstruction process. HYPR may be used in a number of different imaging modalities including magnetic resonance imaging (MRI), x-ray computed tomography (CT), positron emission tomography (PET), single photon emission computed tomography (SPECT) digital tomosynthesis (DTS) and ultrasonic imaging.
As shown in FIG. 1, for example, when a series of time-resolved images 2 are acquired in a dynamic study, each image frame 2 may be reconstructed using a very limited set of acquired views. However, each such set of views is interleaved with the views acquired for other image frames 2, and after a number of image frames have been acquired, a sufficient number of different views are available to reconstruct a quality composite image 3 for use according to the HYPR method. A composite image 3 formed by using all the interleaved projections is thus a much higher quality image (i.e. high signal-to-noise ratio (SNR)), and this higher quality is conveyed to the image frame by using the highly constrained image reconstruction method 4.
A discovery of the HYPR method is that good quality images can be produced with far fewer projection signal profiles if a priori knowledge of the signal contour in the FOV 12 is used in the reconstruction process. Referring to FIG. 2, for example, the signal contour in the FOV 12 may be known to include structures such as blood vessels 18 and 20. That being the case, when the backprojection path 8 passes through these structures a more accurate distribution of the signal sample 14 in each pixel is achieved by weighting the distribution as a function of the known signal contour at that pixel location. As a result, a majority of the signal sample 14 will be distributed in the example of FIG. 2 at the backprojection pixels that intersect the structures 18 and 20. For a backprojection path 8 having N pixels this highly constrained backprojection may be expressed as follows:
                              S          n                =                              (                          P              ×                              C                n                                      )                    /                                    ∑                              n                =                1                            N                        ⁢                                                  ⁢                          C              n                                                          (        2        )            
where: Sn=the backprojected signal magnitude at a pixel n in an image frame being reconstructed;
P=the signal sample value in the projection profile being backprojected; and
Cn=signal value of an a priori composite image at the nth pixel along the backprojection path. The composite image is reconstructed from data acquired during the scan, and may include that used to reconstruct the image frame as well as other acquired image data that depicts the structure in the field of view. The numerator in equation (2) weights each pixel using the corresponding signal value in the composite image and the denominator normalizes the value so that all backprojected signal samples reflect the projection sums for the image frame and are not multiplied by the sum of the composite image.
While the normalization can be performed on each pixel separately after the backprojection, in many clinical applications it is far easier to normalize the projection P before the backprojection. In this case, the projection P is normalized by dividing by the corresponding value Pc in a projection through the composite image at the same view angle. The normalized projection P/Pc is then backprojected and the resulting image is then multiplied by the composite image.
A 3D embodiment of the highly constrained backprojection is shown pictorially in FIG. 3 for a single 3D projection view characterized by the view angles θ and φ. This projection view is back projected along axis 16 and spread into a Radon plane 21 at a distance r along the back projection axis 16. Instead of a filtered back projection in which projection signal values are filtered and uniformly distributed into the successive Radon planes, along axis 16, the projection signal values are distributed in the Radon plane 21 using the information in the composite image. The composite image in the example of FIG. 3 contains vessels 18 and 20. The weighted signal contour value is deposited at image location x, y, z in the Radon plane 21 based on the intensity at the corresponding location x, y, z in the composite image. This is a simple multiplication of the backprojected signal profile value P by the corresponding composite image voxel value. This product is then normalized by dividing the product by the projection profile value from the corresponding image space projection profile formed from the composite image. The formula for the 3D reconstruction isI(x,y,z)=Σ(P(r,θ,φ)*C(x,y,z)(r,θ,φ)/Pc(r,θ,φ)  (2a)where the sum (Σ) is over all projections in the image frame being reconstructed and the x, y, z values in a particular Radon plane are calculated using the projection profile value P(r,θ,φ) at the appropriate r,θ,φ value for that plane. Pc(r,θ,φ) is the corresponding projection profile value from the composite image, and C(x,y,z)r,θ,φ is the composite image value at (r,θ,φ).
The HYPR image reconstruction method has been used primarily to reduce image artifacts due to undersampling in MRI and x-ray CT. However, HYPR can also be used to improve the SNR of an image. For example, the image frames 2 may be acquired in a dynamic study in which the dosage (e.g., x-ray) or exposure time (e.g., PET or SPECT) is reduced for each image frame. In this case the composite image 3 is formed by accumulating or integrating measurements from the series of acquired low SNR image frames 2 to produce a higher SNR composite image 3. In this case, the highly constrained image 4 produced from each low SNR image frame 2 takes on the higher SNR of this composite image 3.
Another HYPR processing method is described in copending U.S. Patent application Ser. No. 60/901,728 filed on Feb. 19, 2007 and entitled “Localized and Highly Constrained Image Reconstruction Method”, the teaching of which is incorporated herein by reference. With this localized HYPR method normalized weighting images are produced from each acquired image frame and each weighting image is multiplied by a high quality composite image which may be formed by accumulating or integrating acquired image frames. Each normalized weighting image is produced by blurring the acquired image frame with a filter and then dividing the blurred image frame with a similarly blurred version of the composite image. This localized HYPR method may be employed as the image reconstruction step where tomographic views of the subject are acquired, or it may be used to enhance the quality of radiograph images by imparting the low artifacts and high SNR qualities of the composite image to the radiograph image.
Regardless of which HYPR processing method is used, when the composite image is formed by integrating a window of acquired image frames, subject motion becomes an issue. If the window is set wide to integrate more image frames and thus produce a higher quality composite image, the composite image may be blurred due to subject motion. When there is substantial subject motion, therefore, the window must be narrowed to avoid blurring and this results in a lower quality composite image and hence lower quality HYPR-produced image frames.